Have you tried just setting the resolution to 1920x1080 or are you literally trying to run AAA games at 4K on a card that was targeting 1080p when it was released, 4 and a half years ago?

I think the best way to handle this would be to just encode everything and upload all files. If I wanted some amount of history, I’d use some file system with automatic snapshots, like ZFS.

If I wanted to do what you’ve outlined, I would probably use rclone with filtering for the extension types or something along those lines.

If I wanted to do this with Git specifically, though, this is what I would try first:

First, add lossless extensions (*.flac, *.wav) to my repo’s .gitignore

Second, schedule a job on my local machine that:

1. Watches for changes to the local file system (e.g., with inotifywait or fswatch)
2. For any new lossless files, if there isn’t already an accompanying lossy files (i.e., identified by being collocated, having the exact same filename, sans extension, with an accepted extension, e.g., .mp3, .ogg - possibly also with a confirmation that the codec is up to my standards with a call to ffprobe, avprobe, mediainfo, exiftool, or something similar), it encodes the file to your preferred lossy format.
3. Use git status --porcelain to if there have been any changes.
4. If so, run git add --all && git commit --message "Automatic commit" && git push
5. Optionally, automatically craft a better commit message by checking which files have been changed, generating text like Added album: "Satin Panthers - EP" by Hudson Mohawke or Removed album: "Brat" by Charli XCX; Added album "Brat and it's the same but there's three more songs so it's not" by Charli XCX

Third, schedule a job on my remote machine server that runs git pull at regular intervals.

One issue with this approach is that if you delete a file (as opposed to moving it), the space is not recovered on your local or your server. If space on your server is a concern, you could work around that by running something like the answer here (adjusting the depth to an appropriate amount for your use case):

git fetch --depth=1
git reflog expire --expire-unreachable=now --all
git gc --aggressive --prune=all

Another potential issue is that what I described above involves having an intermediary git to push to and pull from, e.g., running on a hosted Git forge, like GitHub, Codeberg, etc… This could result in getting copyright complaints or something along those lines, though.

Alternatively, you could use your server as the git server (or check out forgejo if you want a Git forge as well), but then you can’t use the above trick to prune file history and save space from deleted files (on the server, at least - you could on your local, I think). If you then check out your working copy in a way such that Git can use hard links, you should at least be able to avoid needing to store two copies on your server.

The other thing to check out, if you take this approach, is git lfs. EDIT: Actually, I take that back - you probably don’t want to use Git LFS.

It’s the new hyped up version of “no-code” or low-code solutions, but with AI so you have more flexibility to footgun.

Not any lazier. Script kiddies didn’t write the code themselves, either.

It was already known before the whistleblower that:

1. Siri inputs (all STT at that time, really) were processed off device
2. Siri had false activations

The “sinister” thing that we learned was that Apple was reviewing those activations to see if they were false, with the stated intent (as confirmed by the whistleblower) of using them to reduce false activations.

There are also black box methods to verify that data isn’t being sent and that particular hardware (like the microphone) isn’t being used, and there are people who look for vulnerabilities as a hobby. If the microphones on the most/second most popular phone brand (iPhone, Samsung) were secretly recording all the time, evidence of that would be easy to find and would be a huge scoop - why haven’t we heard about it yet?

Snowden and Wikileaks dumped a huge amount of info about governments spying, but nothing in there involved always on microphones in our cell phones.

To be fair, an individual phone is a single compromise away from actually listening to you, so it still makes sense to avoid having sensitive conversations within earshot of a wirelessly connected microphone. But generally that’s not the concern most people should have.

Advertising tracking is much more sinister and complicated and harder to wrap your head around than “my phone is listening to me” and as a result makes for a much less glamorous story, but there are dozens, if not hundreds or thousands, of stories out there about how invasive advertising companies’ methods are, about how they know too much, etc… Think about what LLMs do with text. The level of prediction that they can do. That’s what ML algorithms can do with your behavior.

If you’re misattributing what advertisers know about you to the phone listening and reporting back, then you’re not paying attention to what they’re actually doing.

So yes - be vigilant. Just be vigilant about the right thing.

proven by a whistleblower from apple

Assuming you have an iPhone. And even then, the whistleblower you’re referencing was part of a team who reviewed utterances by users with the “Hey Siri” wake word feature enabled. If you had Siri disabled entirely or had the wake word feature disabled, you weren’t impacted at all.

This may have been limited to impacting only users who also had some option like “Improve Siri and Dictation” enabled, but it’s not clear. Today, the Privacy Policy explicitly says that Apple can have employees review your interactions with Siri and Dictation (my understanding is the reason for the settlement is that they were not explicit that human review was occurring). I strongly recommend disabling that setting, particularly if you have a wake word enabled.

If you have wake words enabled on your phone or device, your phone has to listen to be able to react to them. At that point, of course the phone is listening. Whether it’s sending the info back somewhere is a different story, and there isn’t any evidence that I’m aware of that any major phone company does this.

Sure - Wikipedia says it better than I could hope to:

As English-linguist Larry Andrews describes it, descriptive grammar is the linguistic approach which studies what a language is like, as opposed to prescriptive, which declares what a language should be like.[11]: 25  In other words, descriptive grammarians focus analysis on how all kinds of people in all sorts of environments, usually in more casual, everyday settings, communicate, whereas prescriptive grammarians focus on the grammatical rules and structures predetermined by linguistic registers and figures of power. An example that Andrews uses in his book is fewer than vs less than.[11]: 26  A descriptive grammarian would state that both statements are equally valid, as long as the meaning behind the statement can be understood. A prescriptive grammarian would analyze the rules and conventions behind both statements to determine which statement is correct or otherwise preferable. Andrews also believes that, although most linguists would be descriptive grammarians, most public school teachers tend to be prescriptive.[11]: 26

You might be interested in reading up on the debate of “Prescriptive vs Descriptive” approaches in a linguistics context.

The one I grabbed to test was the ROG Azoth.

I also checked my Iris and Moonlander - both cap out at 6, but I believe I can update that to be higher with QMK or add a config key via Oryx on the Moonlander to turn it on.

deleted by creator

Per this thread from 2009, the limit was conditional upon using a particular keyboard descriptor documented elsewhere in the spec, but keyboards are not required to use that descriptor.

I tested just now on one of my mechanical keyboards, on MacOS, connected via USB C, using the Online Key Rollover Test, and was able to get 44 keys registered at the same time.

If you want to generate audiobooks using your own / a hosted TTS server, check out one of these options:

• OpenReader-WebUI - this has built-in read along capability and can be deployed as a PWA that can allow you to download the audiobooks to your phone so you can use them offline
• p0n1/epub to audiobook
• ebook2audiobook

If you don’t have a decent GPU, Kokoro is a great option as it’s fast enough to run on CPU and still sounds very good.

If you’re going to use Kokoro, Audiblez (posted by another commenter) looks like it makes that more of an all-in-one option.

If you want something that you can use without an upfront building of the audiobook, of the above options, only OpenReader-WebUI supports that. RealtimeTTS is a library that handles that, but I don’t know if there are already any apps out there that integrate it.

If you have the audiobook generation handled and just want to be able to follow along with text / switch between text and audio, check out https://storyteller-platform.gitlab.io/storyteller/

You can run a NAS with any Linux distro - your limiting factor is having enough drive storage. You might want to consider something that’s great at using virtual machines (e.g., Proxmox) if you don’t like Docker, but I have almost everything I want running in Docker and haven’t needed to spin up a single virtual machine.

Copied from the post:

You may have seen reports of leaks of older text messages that had previously been sent to Steam customers. We have examined the leak sample and have determined this was NOT a breach of Steam systems.​

​

We’re still digging into the source of the leak, which is compounded by the fact that any SMS messages are unencrypted in transit, and routed through multiple providers on the way to your phone.​

​

The leak consisted of older text messages that included one-time codes that were only valid for 15-minute time frames and the phone numbers they were sent to. The leaked data did not associate the phone numbers with a Steam account, password information, payment information or other personal data. Old text messages cannot be used to breach the security of your Steam account, and whenever a code is used to change your Steam email or password using SMS, you will receive a confirmation via email and/or Steam secure messages.​

​

You do not need to change your passwords or phone numbers as a result of this event. It is a good reminder to treat any account security messages that you have not explicitly requested as suspicious. We recommend regularly checking your Steam account security at any time at ​

https://store.steampowered.com/account/authorizeddevices​

​

We also recommend setting up the Steam Mobile Authenticator if you haven’t already, as it gives us the best way to send secure messages about your account and your account’s safety.

You don’t have to finish the file to share it though, that’s a major part of bittorrent. Each peer shares parts of the files that they’ve partially downloaded already. So Meta didn’t need to finish and share the whole file to have technically shared some parts of copyrighted works. Unless they just had uploading completely disabled,

The argument was not that it didn’t matter if a user didn’t download the entirety of a work from Meta, but that it didn’t matter whether a user downloaded anything from Meta, regardless of whether Meta was a peer or seed at the time.

Theoretically, Meta could have disabled uploading but not blocked their client from signaling that they could upload. This would, according to that argument, still counts as reproducing the works, under the logic that signaling that it was available is the same as “making it available.”

but they still “reproduced” those works by vectorizing them into an LLM. If Gemini can reproduce a copyrighted work “from memory” then that still counts.

That’s irrelevant to the plaintiff’s argument. And beyond that, it would need to be proven on its own merits. This argument about torrenting wouldn’t be relevant if LLAMA were obviously a derivative creation that wasn’t subject to fair use protections.

It’s also irrelevant if Gemini can reproduce a work, as Meta did not create Gemini.

Does any Llama model reproduce the entirety of The Bedwetter by Sarah Silverman if you provide the first paragraph? Does it even get the first chapter? I highly doubt it.

By the same logic, almost any computer on the internet is guilty of copyright infringement. Proxy servers, VPNs, basically any compute that routed those packets temporarily had (or still has for caches, logs, etc) copies of that protected data.

There have been lawsuits against both ISPs and VPNs in recent years for being complicit in copyright infringement, but that’s a bit different. Generally speaking, there are laws, like the DMCA, that specifically limit the liability of network providers and network services, so long as they respect things like takedown notices.

I’d just like to interject for a moment. What you’re referring to as Alpine Linux Alpine Linux is in fact Pine’s fork, Alpine / Alpine Linux Pine Linux, or as I’ve taken to calling it, Pine’s Alpine plus Alpine Linux Pine Linux. Alpine Linux Pine Linux is an operating system unto itself, and Pine’s Alpine fork is another free component of a fully functioning Alpine Linux Pine Linux system.

According to https://www.nextdiffusion.ai/blogs/hidream-the-new-top-open-source-image-generator it’s an uncensored image generation model developed by Vivago. In the benchmarks they highlighted - DPG-Bench, GenEval, and HPSv2.1 - it was ranked number 1. It’s said to be very good at following complex prompts.

Wow, there isn’t a single solution in here with the obvious answer?

You’ll need a domain name. It doesn’t need to be paid - you can use DuckDNS. Note that whoever hosts your DNS needs to support dynamic DNS. I use Cloudflare for this for free (not their other services) even though I bought my domains from Namecheap.

Then, you can either set up Let’s Encrypt on device and have it generate certs in a location Jellyfin knows about (not sure what this entails exactly, as I don’t use this approach) or you can do what I do:

1. Set up a reverse proxy - I use Traefik but there are a few other solid options - and configure it to use Let’s Encrypt and your domain name.
2. Your reverse proxy should have ports 443 and 80 exposed, but should upgrade http requests to https.
3. Add Jellyfin as a service and route in your reverse proxy’s config.

On your router, forward port 443 to the outbound secure port from your PI (which for simplicity’s sake should also be port 443). You likely also need to forward port 80 in order to verify Let’s Encrypt.

If you want to use Jellyfin while on your network and your router doesn’t support NAT loopback requests, then you can use the server’s IP address and expose Jellyfin’s HTTP ports (e.g., 8080) - just make sure to not forward those ports from the router. You’ll have local unencrypted transfers if you do this, though.

Make sure you have secure passwords in Jellyfin. Note that you are vulnerable to a Jellyfin or Traefik vulnerability if one is found, so make sure to keep your software updated.

If you use Docker, I can share some config info with you on how to set this all up with Traefik, Jellyfin, and a dynamic dns services all up with docker-compose services.

Why should we know this?

Not watching that video for a number of reasons, namely that ten seconds in they hadn’t said anything of substance, their first claim was incorrect (Amazon does not prohibit use of gen ai in books, nor do they require its use be disclosed to the public, no matter how much you might wish it did), and there was nothing in the description of substance, which in instances like this generally means the video will largely be devoid of substance.

What books is the Math Sorcerer selling? Are they the ones on Amazon linked from their page? Are they selling all of those or just promoting most of them?

Why do we think they were generated with AI?

When you say “generated with AI,” what do you mean?

• Generated entirely with AI, without even editing? Then why do they have so many 5 star reviews?
• Generated with AI and then heavily edited?
• Written partly by hand with some pieces written by unedited GenAI?
• Written partly by hand with some pieces written by edited GenAI?
• AI was used for ideation?
• AI was used during editing? E.g., Grammarly?
• GenAI was used during editing?E.g., “ChatGPT, review this chapter and give me any feedback. If sections need rewritten go ahead and take a first pass.”
• AI might have been used, but we don’t know for sure, and the issue is that some passages just “read like AI?”

And what’s the result? Are the books misleading in some way? That’s the most legitimate actual concern I can think of (I’m sure the people screaming that AI isn’t fair use would disagree, but if that’s the concern, settle it in court).

Look up “LLM quantization.” The idea is that each parameter is a number; by default they use 16 bits of precision, but if you scale them into smaller sizes, you use less space and have less precision, but you still have the same parameters. There’s not much quality loss going from 16 bits to 8, but it gets more noticeable as you get lower and lower. (That said, there’s are ternary bit models being trained from scratch that use 1.58 bits per parameter and are allegedly just as good as fp16 models of the same parameter count.)

If you’re using a 4-bit quantization, then you need about half that number in VRAM. Q4_K_M is better than Q4, but also a bit larger. Ollama generally defaults to Q4_K_M. If you can handle a higher quantization, Q6_K is generally best. If you can’t quite fit it, Q5_K_M is generally better than any other option, followed by Q5_K_S.

For example, Llama3.3 70B, which has 70.6 billion parameters, has the following sizes for some of its quantizations:

• q4_K_M (the default): 43 GB
• fp16: 141 GB
• q8: 75 GB
• q6_K: 58 GB
• q5_k_m: 50 GB
• q4: 40 GB
• q3_K_M: 34 GB
• q2_K: 26 GB

This is why I run a lot of Q4_K_M 70B models on two 3090s.

Generally speaking, there’s not a perceptible quality drop going to Q6_K from 8 bit quantization (though I have heard this is less true with MoE models). Below Q6, there’s a bit of a drop between it and 5 and then 4, but the model’s still decent. Below 4-bit quantizations you can generally get better results from a smaller parameter model at a higher quantization.

TheBloke on Huggingface has a lot of GGUF quantization repos, and most, if not all of them, have a blurb about the different quantization types and which are recommended. When Ollama.com doesn’t have a model I want, I’m generally able to find one there.